3D Face Alignment
12 papers with code • 1 benchmarks • 2 datasets
How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets.
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge.
To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment.
Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector.
3D face alignment of monocular images is a crucial process in the recognition of faces with disguise. 3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference. This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework. We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism.
Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators.